Energy-Efficient Content Fetching Strategies in Cache-Enabled D2D Networks via an Actor-Critic Reinforcement Learning Structure

Abstract

As one of the important complementary technologies of the fifth-generation (5G) wireless communication and beyond, mobile device-to-device (D2D) edge caching and computing can effectively reduce the pressure on backbone networks and improve the user experience. Specific content can be pre-cached on the user devices based on personalized content placement strategies, and the cached content can be fetched by neighboring devices in the same D2D network. However, when multiple devices simultaneously fetch content from the same device, collisions will occur and reduce communication efficiency. In this paper, we design the content fetching strategies based on an actor-critic deep reinforcement lea..